Package for curating data annotation efforts.
pyAnno 2.0 is a Python library for the analysis and diagnostic testing of annotation and curation efforts. pyAnno implements statistical models for inferring from categorical data annotated by multiple annotators
annotator accuracies and biases,
gold standard categories of items,
prevalence of categories in population, and
population distribution of annotator accuracies and biases.
The models include a generalization of Dawid and Skene’s (1979) multinomial model with Dirichlet priors on prevalence and estimator accuracy, and the two models introduces in Rzhetsky et al.’s (2009). The implementation allows Maximum Likelihood and Maximum A Posteriori estimation of parameters, and to draw samples from the full posterior distribution over annotator accuracy.
pyAnno is licensed under a modified BSD license (2-clause). For more information, see
The documentation is hosted at http://github.enthought.com/uchicago-pyanno/ .
Pietro Berkes (Enthought, Ltd.)
Bob Carpenter (Columbia University, Statistics)
Andrey Rzhetsky (University of Chicago, Medicine)
James Evans (University of Chicago, Sociology)
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